Project Summary Calcium imaging methods allow us to record the simultaneous activity of many neurons with single-cell resolution; these methods are therefore a critical enabling tool for the BRAIN initiative and in neuroscience more broadly. These experiments produce enormous 2D or 3D video datasets ? in some cases with data rates measured in terabytes/hour - and the analysis of this ?big data? currently represents a major bottleneck on scientific progress in this field. This project develops powerful new analysis methods for eliminating this bottleneck, opening up new scientific questions and applications that can be attacked with these new tools. The methods under development simultaneously identify the locations of the imaged neurons, resolve spatially overlapping neuronal shapes, and provide denoised estimates of the activity of each neuron, with minimal manual parameter tuning. The new methods quantitatively and qualitatively improve upon the state of the art in both simulated data and in a wide variety of real data applications, leading to the recovery of useful signals from many more neurons than otherwise possible. At the same time, the methods are computationally scalable and modular, enabling a healthy user and development community. Finally, the methods are extensible: they are founded on well-defined probabilistic modeling and convex optimization principles, enabling a range of extensions to address important new scientific problems. Specific aims of the project include a number of critical subprojects focused on: first, scaling up these methods to handle very large data sets, as computationally efficiently as possible, to enable closed-loop, real-time experiments; and second, strengthening the methods to obtain statistically optimal solutions, in order to extract as much information from the data as possible, with the highest possible spatiotemporal resolution, enabling the development of novel integrated computational imaging methods. In parallel, this project will develop extensions of these methods to handle different data types: spatially blurred data, or data formed via some more complicated linear imaging transformation (e.g., from light-field cameras); imaging data in which we can constrain and improve our results by exploiting simultaneously-recorded stimulus or behavioral information; and finally, imaging data recorded simultaneously with high-temporal-resolution multielectrode electrical data, in order to combine the strengths of these two data types. The proposed analytical tools will be widely used in the neuroscience community, and will have a strong influence on fundamental approaches to understanding neuroscience data; furthermore, the project will inform experimental paradigms and drive future data collection.